class: center, middle, inverse, title-slide .title[ # Introduction to R for Data Analysis ] .subtitle[ ## Data Types, Import & Export ] .author[ ### Johannes Breuer, Stefan Jünger & Veronika Batzdorfer ] .date[ ### 2021-08-02 ] --- layout: true --- ## Getting data into `R` Thus far, we've already learned what `R` and `RStudio` are. This course is about starting to use `R` and feeling prepared to use it for statistical analyses. There's one essential prerequisite: .center[**We need data!**] <img src="data:image/png;base64,#../img/import_data.png" width="50%" style="display: block; margin: auto;" /> --- ## Content of this session - What are `R`'s internal data types? - How to work with different data types? - How to import data in different formats? - How to export data in different formats --- ## Data we use in this course During the course, we use several different data sets. Especially in this session, where we apply different importing functions, we quite a few data sets, from data about the Titanic to data about unicorns. However, we will also use data that are more interesting for social and behavioral scientists. --- ## It boils all down to... .pull-left[ **How your data are stored (data types)** - 'Numbers' (Integers & Doubles) - Character Strings - Logical - Factors - ... - There's more, e.g., expressions, but let's leave it at that ] .pull-right[ **Where your data are stored (data formats)** - Vectors - Matrices - Arrays - Data frames / Tibbles - Lists ] .footnote[https://www.stat.berkeley.edu/~nolan/stat133/Fall05/lectures/DataTypes4.pdf] --- ## Numeric data .small[ *Integers* are values without a decimal value. To be explicit in `R` in using them, you have to place an `L` behind the actual value. ```r 1L ``` ``` ## [1] 1 ``` By contrast, *doubles* are values with a decimal value. ```r 1.1 ``` ``` ## [1] 1.1 ``` We can check data types by using the `typeof()` function. ```r typeof(1L) ``` ``` ## [1] "integer" ``` ```r typeof(1.1) ``` ``` ## [1] "double" ``` ] --- ## Character strings At first glance, a *character* is a letter somewhere between a-z. *String* in this context might mean that we have a series of characters. However, numbers and other symbols can be part of a *character string*, which can then be, e.g., part of a text. In `R`, character strings are wrapped in quotation marks. ```r "Hi. I am a character string, the 1st of its kind!" ``` ``` ## [1] "Hi. I am a character string, the 1st of its kind!" ``` *Note*: There are no values associated with the content of character strings unless we change that, e.g., with factors. --- ## Factors If you're a *Stata* (or *SPSS*) user, you may already be familiar with factors. Factors are data types that assume that their values are not continuous, e.g., as in [ordinal](https://en.wikipedia.org/wiki/Level_of_measurement#Ordinal_scale) or [nominal](https://en.wikipedia.org/wiki/Level_of_measurement#Nominal_level) data. ```r factor(1.1) ``` ``` ## [1] 1.1 ## Levels: 1.1 ``` ```r factor("Hi. I am a character string, the 1st of its kind!") ``` ``` ## [1] Hi. I am a character string, the 1st of its kind! ## Levels: Hi. I am a character string, the 1st of its kind! ``` Factors take numeric data or character strings as input as they simply convert them into so-called levels. This concept may be a little bit abstract for the time being. It's just essential to have heard about them before you learn more about them. --- ## Logical values Logical values are basically either `TRUE` or `FALSE` values. These values are produced by making logical requests on your data. ```r 2 > 1 ``` ``` ## [1] TRUE ``` ```r 2 < 1 ``` ``` ## [1] FALSE ``` Logical values are at the heart of creating loops. For this purpose, however, we need more logical operators to request `TRUE` or `FALSE` values. --- ## Logical operators There are quite a few logical operators in `R`: .pull-left[ - `<` less than - `<=` less than or equal to - `>` greater than - `>=` greater than or equal to - `== ` exactly equal to - `!=` not equal to ] .pull-right[ - `!x` Not x - `x | y` x OR y - `x & y ` x AND y - `isTRUE(x)` test if X is TRUE - `isFALSE(x)` test if X is FALSE ] .footnote[https://www.statmethods.net/management/operators.html] Moreover, there are some more `is.PROPERTY_ASKED_FOR()` functions, such as `is.numeric()`, which also return `TRUE` or `FALSE` values. --- ## `R`'s data formats `R`'s different data types can be put into 'containers'. <img src="data:image/png;base64,#../img/9213.1526125966.png" width="75%" style="display: block; margin: auto;" /> .footnote[https://devopedia.org/r-data-structures] --- ## Vectors Vectors are built by enclosing your content with `c()` ("c" for "concatenate") ```r numeric_vector <- c(1, 2, 3, 4) character_vector <- c("a", "b", "c", "d") numeric_vector ``` ``` ## [1] 1 2 3 4 ``` ```r character_vector ``` ``` ## [1] "a" "b" "c" "d" ``` Vectors are really like vectors in mathematics. Initially, it doesn't matter if you look at them as column or row vectors. --- ## ...but it matters when you combine vectors Using the function `cbind()` or `rbind()` you can either combine vectors column-wise or row-wise. Thus, they become matrices. ```r cbind(numeric_vector, character_vector) ``` ``` ## numeric_vector character_vector ## [1,] "1" "a" ## [2,] "2" "b" ## [3,] "3" "c" ## [4,] "4" "d" ``` ```r rbind(numeric_vector, character_vector) ``` ``` ## [,1] [,2] [,3] [,4] ## numeric_vector "1" "2" "3" "4" ## character_vector "a" "b" "c" "d" ``` .small[ *Note*: The numeric values are [coerced](https://www.oreilly.com/library/view/r-in-a/9781449358204/ch05s08.html) into strings here. ] --- ## Matrices Matrices are the basic rectangular data format in R. ```r fancy_matrix <- matrix(1:16, nrow = 4) fancy_matrix ``` ``` ## [,1] [,2] [,3] [,4] ## [1,] 1 5 9 13 ## [2,] 2 6 10 14 ## [3,] 3 7 11 15 ## [4,] 4 8 12 16 ``` You cannot store multiple data types, such as strings and numeric values in the same matrix. Otherwise, your data will get coerced to a common type, as seen in the previous slide. This is something that happens already within vectors: ```r c(1, 2, "evil string") ``` ``` ## [1] "1" "2" "evil string" ``` --- ## Data frames While matrices are used, e.g.,--\*drumroll\*-- for matrix operations, data frames resemble more the data formats most of you are probably already familiar with. We can build data frames by hand as here: .tinyish[ ```r library(randomNames) # a name generator package fancy_data <- data.frame( who = randomNames(n = 10, which.names = "first"), age = sample(14:49, 10, replace = TRUE), # you see what we are doing here? salary_2018 = sample(15:100, 10, replace = TRUE), salary_2019 = sample(15:100, 10, replace = TRUE) ) fancy_data ``` ] .right[↪️] --- class: middle ``` ## who age salary_2018 salary_2019 ## 1 Jordan 29 95 28 ## 2 Taylor 30 35 68 ## 3 Lessa 37 88 89 ## 4 Dante 48 91 15 ## 5 Mychal 30 85 44 ## 6 Benjamin 21 78 35 ## 7 Laura 25 68 41 ## 8 Karen 46 77 19 ## 9 Araseli 35 92 69 ## 10 Taylor 17 61 80 ``` --- ## Tibbles .pull-left[ Tibbles are basically just `R data.frames` but nicer. - only the first ten observations are printed - the output is tidier! - you get some additional metadata about rows and columns that you would normally only get when using `dim()` and other functions You can check the [tibble vignette](https://cran.r-project.org/web/packages/tibble/vignettes/tibble.html) for technical details. ] .pull-right[ <img src="data:image/png;base64,#../img/tibble.png" width="60%" style="display: block; margin: auto;" /> ] --- ## Tibble conversion ```r library(tibble) as_tibble(fancy_data) ``` ``` ## # A tibble: 10 × 4 ## who age salary_2018 salary_2019 ## <chr> <int> <int> <int> ## 1 Jordan 29 95 28 ## 2 Taylor 30 35 68 ## 3 Lessa 37 88 89 ## 4 Dante 48 91 15 ## 5 Mychal 30 85 44 ## 6 Benjamin 21 78 35 ## 7 Laura 25 68 41 ## 8 Karen 46 77 19 ## 9 Araseli 35 92 69 ## 10 Taylor 17 61 80 ``` --- ## One last type you should know: lists Lists are perfect for storing numerous and potentially diverse pieces of information in one place. ```r fancy_list <- list( numeric_vector, character_vector, fancy_matrix, fancy_data ) fancy_list ``` .right[↪️] --- class: middle .tinyish[ ``` ## [[1]] ## [1] 1 2 3 4 ## ## [[2]] ## [1] "a" "b" "c" "d" ## ## [[3]] ## [,1] [,2] [,3] [,4] ## [1,] 1 5 9 13 ## [2,] 2 6 10 14 ## [3,] 3 7 11 15 ## [4,] 4 8 12 16 ## ## [[4]] ## who age salary_2018 salary_2019 ## 1 Jordan 29 95 28 ## 2 Taylor 30 35 68 ## 3 Lessa 37 88 89 ## 4 Dante 48 91 15 ## 5 Mychal 30 85 44 ## 6 Benjamin 21 78 35 ## 7 Laura 25 68 41 ## 8 Karen 46 77 19 ## 9 Araseli 35 92 69 ## 10 Taylor 17 61 80 ``` ] --- ## Nested lists ```r fancy_nested_list <- list( fancy_vectors = list(numeric_vector, character_vector), data_stuff = list(fancy_matrix, fancy_data) ) fancy_nested_list ``` .right[↪️] --- class: middle .tinyish[ ``` ## $fancy_vectors ## $fancy_vectors[[1]] ## [1] 1 2 3 4 ## ## $fancy_vectors[[2]] ## [1] "a" "b" "c" "d" ## ## ## $data_stuff ## $data_stuff[[1]] ## [,1] [,2] [,3] [,4] ## [1,] 1 5 9 13 ## [2,] 2 6 10 14 ## [3,] 3 7 11 15 ## [4,] 4 8 12 16 ## ## $data_stuff[[2]] ## who age salary_2018 salary_2019 ## 1 Jordan 29 95 28 ## 2 Taylor 30 35 68 ## 3 Lessa 37 88 89 ## 4 Dante 48 91 15 ## 5 Mychal 30 85 44 ## 6 Benjamin 21 78 35 ## 7 Laura 25 68 41 ## 8 Karen 46 77 19 ## 9 Araseli 35 92 69 ## 10 Taylor 17 61 80 ``` ] --- ## Accessing elements by index Generally, the logic of `[index_number]` is used in `R` to access only a subset of information in an object, no matter if we have vectors or data frames. Say, we want to extract the second element of our `character_vector` object, we could do that like this: ```r character_vector[2] ``` ``` ## [1] "b" ``` --- ## More complicated cases: matrices Matrices can have more dimensions, often you want information from a specific row and column. ```r a_wonderful_matrix[number_of_row, number_of_column] ``` *Note*: You can do the same indexing with `data.frame`s. We will talk more about this in the session on *Data Wrangling Basics*. --- ## Matrices and subscripts (as in mathematical notation) Identifying rows, columns, or elements using subscripts is similar to matrix notation: ```r fancy_matrix[, 4] # 4th column of matrix fancy_matrix[3,] # 3rd row of matrix fancy_matrix[2:4, 1:3] # rows 2,3,4 of columns 1,2,3 ``` It's really like in math, and you can perform standard mathematical operations, such as matrix multiplications. ```r fancy_matrix[2:4, 1:3] %*% fancy_matrix[1:3, 2:4] ``` ``` ## [,1] [,2] [,3] ## [1,] 116 188 260 ## [2,] 134 218 302 ## [3,] 152 248 344 ``` --- ## The case of data frames A nice feature of `data.frames` or `tibbles` is that their columns are names, just as variable names in ordinary data. It would be cumbersome to use index numbers to extract a specific column/variable, right? Do not fear: ```r fancy_data$who ``` ``` ## [1] "Jordan" "Taylor" "Lessa" "Dante" "Mychal" "Benjamin" "Laura" "Karen" "Araseli" "Taylor" ``` Just place a `$`-sign between the data object and the variable name. --- ## `[]` in data frames Sometimes we also have to rely on character strings as input information, e.g., for iterating over data. We can also use `[]` to access variables by name. .pull-left[ Not only this way: ```r fancy_data[1] ``` ``` ## who ## 1 Jordan ## 2 Taylor ## 3 Lessa ## 4 Dante ## 5 Mychal ## 6 Benjamin ## 7 Laura ## 8 Karen ## 9 Araseli ## 10 Taylor ``` ] .pull-right[ But also this way: ```r fancy_data["who"] ``` ``` ## who ## 1 Jordan ## 2 Taylor ## 3 Lessa ## 4 Dante ## 5 Mychal ## 6 Benjamin ## 7 Laura ## 8 Karen ## 9 Araseli ## 10 Taylor ``` ] --- ## Difference between `[]` and `[[]]` https://twitter.com/hadleywickham/status/643381054758363136 --- ## Data frame check 1, 2, 1, 2! Once you start working with data in `R` a good first thing to do is to have a quick look at them. The most high-level information you can get is about the object type and its dimensions. .small[ ```r # object type class(fancy_data) ``` ``` ## [1] "data.frame" ``` ```r # number of rows and columns dim(fancy_data) ``` ``` ## [1] 10 4 ``` ```r # number of rows nrow(fancy_data) ``` ``` ## [1] 10 ``` ```r # number of columns ncol(fancy_data) ``` ``` ## [1] 4 ``` ] --- ## Data frame check 1, 2, 1, 2! You can also print the first 6 lines of the data frame with `head()`. You can easily change the number of lines by providing the number as the second argument to the `head()` function. ```r head(fancy_data, 3) ``` ``` ## who age salary_2018 salary_2019 ## 1 Jordan 29 95 28 ## 2 Taylor 30 35 68 ## 3 Lessa 37 88 89 ``` --- ## Data frame check 1, 2, 1, 2! If we want some more (detailed) information about the data set or object, we can use the `base R` function `str()`. ```r str(fancy_data) ``` ``` ## 'data.frame': 10 obs. of 4 variables: ## $ who : chr "Jordan" "Taylor" "Lessa" "Dante" ... ## $ age : int 29 30 37 48 30 21 25 46 35 17 ## $ salary_2018: int 95 35 88 91 85 78 68 77 92 61 ## $ salary_2019: int 28 68 89 15 44 35 41 19 69 80 ``` --- ## Data frame check 1, 2, 1, 2! If you want to have a look at your full data set, you can use the `View()` function. In *RStudio*, this will open a new tab in the source pane through which you can explore the data set (including a search function). You can also click on the small spreadsheet symbol on the right side of the object in the environment tab to open this view. ```r View(fancy_data) ``` <img src="data:image/png;base64,#../img/rstudio_view.png" width="65%" style="display: block; margin: auto;" /> --- ## Viewing and changing names We can print all names of an object using the `names()` function... ```r names(fancy_data) ``` ``` ## [1] "who" "age" "salary_2018" "salary_2019" ``` ...and we can also change names with it. ```r names(fancy_data) <- c("name", "age", "salary_2018", "salary_2019") names(fancy_data) ``` ``` ## [1] "name" "age" "salary_2018" "salary_2019" ``` However, there are more flexible ways of doing this as we will see in the session on *Data Wrangling Basics* tomorrow. --- class: center, middle # [Exercise](https://jobreu.github.io/r-intro-gesis-2021/exercises/Exercise_1_2_1_Data_Types.html) time 🏋️‍♀️💪🏃🚴 ## [Solutions](https://jobreu.github.io/r-intro-gesis-2021/solutions/Exercise_1_2_1_Data_Types.html) --- ## German General Social Survey 2021 (GGSS/ALLBUS) .left-column[ <img src="data:image/png;base64,#../img/allbus.png" style="display: block; margin: auto;" /> ] .right-column[ For most of the examples and exercises in this course we will use data from the [German General Social Survey 2021 (GGSS/ALLBUS)](https://www.gesis.org/en/institute/research-data-centers/rdc-allbus). You can [download the data set in different formats as well as the codebook and the questionnaire (in German) from the *GESIS* website](https://search.gesis.org/research_data/ZA5280) (note: you need to have/create a user account). Theresa also prepared a subset documentation of variables that might be interesting for you in the `./data` folder. The *GGSS* website provides [detailed documentation](https://www.gesis.org/en/allbus/contents-search). ] --- ## Gapminder Data .left-column[ <img src="data:image/png;base64,#../img/gapminder_logo.png" style="display: block; margin: auto;" /> ] .right-column[ We will also use [data from *Gapminder*](https://www.gapminder.org/data/). During the course and the exercises, we work with data we have downloaded from their website. There also is an `R` package that bundles some of the *Gapminder* data: `install.packages("gapminder")`. This `R` package provides ["[a]n excerpt of the data available at Gapminder.org. For each of 142 countries, the package provides values for life expectancy, GDP per capita, and population, every five years, from 1952 to 2007."](https://cran.r-project.org/web/packages/gapminder/index.html) ] --- ## How to use the data in general To code along and be able to do the exercises, you should store the data files for the *GGSS 2021* in a folder called `./data` in the same folder as the other materials for this course. --- ## `R` is data-agnostic <img src="data:image/png;base64,#../img/Datenimport.PNG" width="65%" style="display: block; margin: auto;" /> --- ## Data formats & packages .pull-left[ **What you will learn** - Getting the most common data formats into `R` - e.g., CSV, *Stata*, *SPSS*, or *Excel* spreadsheets - Using the different methods of doing that - We will rely a lot on packages and functions from the `tidyverse` instead of using `base R` ] .pull-right[ **What you won't learn** - Getting old & obscure binary data formats into `R` - ... although [that is possible](https://cran.r-project.org/doc/manuals/r-release/R-data.html) ] --- ## Before writing any code: *RStudio* functionality for importing data You can use the *RStudio* GUI for importing data via `Environment - Import data set - Choose file type`. <img src="data:image/png;base64,#../img/rstudio_import.PNG" style="display: block; margin: auto;" /> --- ## Where to find data **Browse Button in `RStudio`** <img src="data:image/png;base64,#../img/importBrowse.PNG" width="75%" style="display: block; margin: auto;" /> **Code preview in `Rstudio`** <img src="data:image/png;base64,#../img/codepreview.PNG" width="75%" style="display: block; margin: auto;" /> --- ## Honestly, after some time you will write the code directly .center[ <img src="data:image/png;base64,#../img/coding_cat.gif" style="display: block; margin: auto;" /> .footnote[[Source](https://media.giphy.com/media/LmNwrBhejkK9EFP504/source.gif)] ] --- ## Honestly, after some time you will write the code directly .center[ <img src="data:image/png;base64,#../img/hadley-typing.gif" style="display: block; margin: auto;" /> [Source](https://tenor.com/view/hadley-wickham-rstats-typing-rcode-gif-11365139) ] --- ## Simple vs. not so simple file formats Basic file formats, such as CSV (comma-separated value file), can directly be imported into `R` - they are 'flat' - few metadata - basically text files Other file formats, particularly the proprietary ones, require the use of additional packages - they are complex - a lot of metadata (think of all the labels in an *SPSS* file) - they are binary (1110101) --- ## File formats wars <img src="data:image/png;base64,#../img/norm_normal_file_format.png" width="30%" style="display: block; margin: auto;" /> https://xkcd.com/2116/ --- ## Disclaimer **In the following slides, we'll jump right into importing data. We use a lot of different packages for this purpose, and you don't have to remember everything. It's just for making a point of how agnostic `R` actually is regarding the file type. Later on, we will dive more into the specifics of importing.** --- ## Importing a CSV file using `base R` ```r titanic <- read.csv("./data/titanic.csv") titanic ``` .tinyish[ ``` ## PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare ## 1 1 0 3 Braund, Mr. Owen Harris male 22.00 1 0 A/5 21171 7.2500 ## 2 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38.00 1 0 PC 17599 71.2833 ## 3 3 1 3 Heikkinen, Miss. Laina female 26.00 0 0 STON/O2. 3101282 7.9250 ## 4 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.00 1 0 113803 53.1000 ## 5 5 0 3 Allen, Mr. William Henry male 35.00 0 0 373450 8.0500 ## 6 6 0 3 Moran, Mr. James male NA 0 0 330877 8.4583 ## 7 7 0 1 McCarthy, Mr. Timothy J male 54.00 0 0 17463 51.8625 ## 8 8 0 3 Palsson, Master. Gosta Leonard male 2.00 3 1 349909 21.0750 ## 9 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.00 0 2 347742 11.1333 ## 10 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14.00 1 0 237736 30.0708 ## 11 11 1 3 Sandstrom, Miss. Marguerite Rut female 4.00 1 1 PP 9549 16.7000 ## 12 12 1 1 Bonnell, Miss. Elizabeth female 58.00 0 0 113783 26.5500 ## 13 13 0 3 Saundercock, Mr. William Henry male 20.00 0 0 A/5. 2151 8.0500 ## 14 14 0 3 Andersson, Mr. Anders Johan male 39.00 1 5 347082 31.2750 ## 15 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina female 14.00 0 0 350406 7.8542 ## 16 16 1 2 Hewlett, Mrs. (Mary D Kingcome) female 55.00 0 0 248706 16.0000 ## 17 17 0 3 Rice, Master. Eugene male 2.00 4 1 382652 29.1250 ## 18 18 1 2 Williams, Mr. Charles Eugene male NA 0 0 244373 13.0000 ## 19 19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele) female 31.00 1 0 345763 18.0000 ## 20 20 1 3 Masselmani, Mrs. Fatima female NA 0 0 2649 7.2250 ## 21 21 0 2 Fynney, Mr. Joseph J male 35.00 0 0 239865 26.0000 ## 22 22 1 2 Beesley, Mr. Lawrence male 34.00 0 0 248698 13.0000 ## 23 23 1 3 McGowan, Miss. Anna "Annie" female 15.00 0 0 330923 8.0292 ## 24 24 1 1 Sloper, Mr. William Thompson male 28.00 0 0 113788 35.5000 ## 25 25 0 3 Palsson, Miss. Torborg Danira female 8.00 3 1 349909 21.0750 ## 26 26 1 3 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson) female 38.00 1 5 347077 31.3875 ## 27 27 0 3 Emir, Mr. Farred Chehab male NA 0 0 2631 7.2250 ## 28 28 0 1 Fortune, Mr. Charles Alexander male 19.00 3 2 19950 263.0000 ## 29 29 1 3 O'Dwyer, Miss. Ellen "Nellie" female NA 0 0 330959 7.8792 ## 30 30 0 3 Todoroff, Mr. Lalio male NA 0 0 349216 7.8958 ## 31 31 0 1 Uruchurtu, Don. Manuel E male 40.00 0 0 PC 17601 27.7208 ## 32 32 1 1 Spencer, Mrs. William Augustus (Marie Eugenie) female NA 1 0 PC 17569 146.5208 ## 33 33 1 3 Glynn, Miss. Mary Agatha female NA 0 0 335677 7.7500 ## 34 34 0 2 Wheadon, Mr. Edward H male 66.00 0 0 C.A. 24579 10.5000 ## 35 35 0 1 Meyer, Mr. Edgar Joseph male 28.00 1 0 PC 17604 82.1708 ## 36 36 0 1 Holverson, Mr. Alexander Oskar male 42.00 1 0 113789 52.0000 ## 37 37 1 3 Mamee, Mr. Hanna male NA 0 0 2677 7.2292 ## 38 38 0 3 Cann, Mr. Ernest Charles male 21.00 0 0 A./5. 2152 8.0500 ## 39 39 0 3 Vander Planke, Miss. Augusta Maria female 18.00 2 0 345764 18.0000 ## 40 40 1 3 Nicola-Yarred, Miss. Jamila female 14.00 1 0 2651 11.2417 ## 41 41 0 3 Ahlin, Mrs. Johan (Johanna Persdotter Larsson) female 40.00 1 0 7546 9.4750 ## 42 42 0 2 Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott) female 27.00 1 0 11668 21.0000 ## 43 43 0 3 Kraeff, Mr. Theodor male NA 0 0 349253 7.8958 ## 44 44 1 2 Laroche, Miss. Simonne Marie Anne Andree female 3.00 1 2 SC/Paris 2123 41.5792 ## 45 45 1 3 Devaney, Miss. Margaret Delia female 19.00 0 0 330958 7.8792 ## 46 46 0 3 Rogers, Mr. William John male NA 0 0 S.C./A.4. 23567 8.0500 ## 47 47 0 3 Lennon, Mr. Denis male NA 1 0 370371 15.5000 ## 48 48 1 3 O'Driscoll, Miss. Bridget female NA 0 0 14311 7.7500 ## 49 49 0 3 Samaan, Mr. Youssef male NA 2 0 2662 21.6792 ## 50 50 0 3 Arnold-Franchi, Mrs. Josef (Josefine Franchi) female 18.00 1 0 349237 17.8000 ## 51 51 0 3 Panula, Master. Juha Niilo male 7.00 4 1 3101295 39.6875 ## 52 52 0 3 Nosworthy, Mr. Richard Cater male 21.00 0 0 A/4. 39886 7.8000 ## 53 53 1 1 Harper, Mrs. Henry Sleeper (Myna Haxtun) female 49.00 1 0 PC 17572 76.7292 ## 54 54 1 2 Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson) female 29.00 1 0 2926 26.0000 ## 55 55 0 1 Ostby, Mr. Engelhart Cornelius male 65.00 0 1 113509 61.9792 ## 56 56 1 1 Woolner, Mr. Hugh male NA 0 0 19947 35.5000 ## 57 57 1 2 Rugg, Miss. Emily female 21.00 0 0 C.A. 31026 10.5000 ## 58 58 0 3 Novel, Mr. Mansouer male 28.50 0 0 2697 7.2292 ## 59 59 1 2 West, Miss. Constance Mirium female 5.00 1 2 C.A. 34651 27.7500 ## 60 60 0 3 Goodwin, Master. William Frederick male 11.00 5 2 CA 2144 46.9000 ## 61 61 0 3 Sirayanian, Mr. Orsen male 22.00 0 0 2669 7.2292 ## 62 62 1 1 Icard, Miss. Amelie female 38.00 0 0 113572 80.0000 ## 63 63 0 1 Harris, Mr. Henry Birkhardt male 45.00 1 0 36973 83.4750 ## 64 64 0 3 Skoog, Master. Harald male 4.00 3 2 347088 27.9000 ## 65 65 0 1 Stewart, Mr. Albert A male NA 0 0 PC 17605 27.7208 ## 66 66 1 3 Moubarek, Master. Gerios male NA 1 1 2661 15.2458 ## 67 67 1 2 Nye, Mrs. (Elizabeth Ramell) female 29.00 0 0 C.A. 29395 10.5000 ## 68 68 0 3 Crease, Mr. Ernest James male 19.00 0 0 S.P. 3464 8.1583 ## 69 69 1 3 Andersson, Miss. Erna Alexandra female 17.00 4 2 3101281 7.9250 ## 70 70 0 3 Kink, Mr. Vincenz male 26.00 2 0 315151 8.6625 ## 71 71 0 2 Jenkin, Mr. Stephen Curnow male 32.00 0 0 C.A. 33111 10.5000 ## 72 72 0 3 Goodwin, Miss. Lillian Amy female 16.00 5 2 CA 2144 46.9000 ## 73 73 0 2 Hood, Mr. Ambrose Jr male 21.00 0 0 S.O.C. 14879 73.5000 ## 74 74 0 3 Chronopoulos, Mr. Apostolos male 26.00 1 0 2680 14.4542 ## 75 75 1 3 Bing, Mr. Lee male 32.00 0 0 1601 56.4958 ## 76 76 0 3 Moen, Mr. Sigurd Hansen male 25.00 0 0 348123 7.6500 ## 77 77 0 3 Staneff, Mr. Ivan male NA 0 0 349208 7.8958 ## 78 78 0 3 Moutal, Mr. Rahamin Haim male NA 0 0 374746 8.0500 ## 79 79 1 2 Caldwell, Master. Alden Gates male 0.83 0 2 248738 29.0000 ## 80 80 1 3 Dowdell, Miss. Elizabeth female 30.00 0 0 364516 12.4750 ## 81 81 0 3 Waelens, Mr. Achille male 22.00 0 0 345767 9.0000 ## 82 82 1 3 Sheerlinck, Mr. Jan Baptist male 29.00 0 0 345779 9.5000 ## 83 83 1 3 McDermott, Miss. Brigdet Delia female NA 0 0 330932 7.7875 ## Cabin Embarked ## 1 S ## 2 C85 C ## 3 S ## 4 C123 S ## 5 S ## 6 Q ## 7 E46 S ## 8 S ## 9 S ## 10 C ## 11 G6 S ## 12 C103 S ## 13 S ## 14 S ## 15 S ## 16 S ## 17 Q ## 18 S ## 19 S ## 20 C ## 21 S ## 22 D56 S ## 23 Q ## 24 A6 S ## 25 S ## 26 S ## 27 C ## 28 C23 C25 C27 S ## 29 Q ## 30 S ## 31 C ## 32 B78 C ## 33 Q ## 34 S ## 35 C ## 36 S ## 37 C ## 38 S ## 39 S ## 40 C ## 41 S ## 42 S ## 43 C ## 44 C ## 45 Q ## 46 S ## 47 Q ## 48 Q ## 49 C ## 50 S ## 51 S ## 52 S ## 53 D33 C ## 54 S ## 55 B30 C ## 56 C52 S ## 57 S ## 58 C ## 59 S ## 60 S ## 61 C ## 62 B28 ## 63 C83 S ## 64 S ## 65 C ## 66 C ## 67 F33 S ## 68 S ## 69 S ## 70 S ## 71 S ## 72 S ## 73 S ## 74 C ## 75 S ## 76 F G73 S ## 77 S ## 78 S ## 79 S ## 80 S ## 81 S ## 82 S ## 83 Q ## [ reached 'max' / getOption("max.print") -- omitted 808 rows ] ``` ] --- ## A `readr` example: `CSV` files ```r library(readr) titanic <- read_csv("./data/titanic.csv") ``` --- class: middle .tinyish[ ```r titanic ``` ``` ## # A tibble: 891 × 12 ## PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked ## <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <chr> <chr> ## 1 1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.25 <NA> S ## 2 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0 PC 17599 71.3 C85 C ## 3 3 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7.92 <NA> S ## 4 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 113803 53.1 C123 S ## 5 5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.05 <NA> S ## 6 6 0 3 Moran, Mr. James male NA 0 0 330877 8.46 <NA> Q ## 7 7 0 1 McCarthy, Mr. Timothy J male 54 0 0 17463 51.9 E46 S ## 8 8 0 3 Palsson, Master. Gosta Leonard male 2 3 1 349909 21.1 <NA> S ## 9 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27 0 2 347742 11.1 <NA> S ## 10 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14 1 0 237736 30.1 <NA> C ## # … with 881 more rows ## # ℹ Use `print(n = ...)` to see more rows ``` ] Note the column specifications: `readr` 'guesses' them based on the first 1000 observations (we will come back to this later). --- ## Importing *Excel* files with `readxl` ```r library(readxl) unicorns <- read_xlsx("./data/observations.xlsx") ``` No output ☹️ --- class: middle ```r unicorns ``` ``` ## # A tibble: 42 × 3 ## countryname year pop ## <chr> <dbl> <dbl> ## 1 Austria 1670 85 ## 2 Austria 1671 83 ## 3 Austria 1674 75 ## 4 Austria 1675 82 ## 5 Austria 1676 79 ## 6 Austria 1677 70 ## 7 Austria 1678 81 ## 8 Austria 1680 80 ## 9 France 1673 70 ## 10 France 1674 79 ## # … with 32 more rows ## # ℹ Use `print(n = ...)` to see more rows ``` --- ## *Stata* files with `haven` ```r library(haven) allbus_2021_stata <- read_stata("./data/allbus_2021/ZA5280_v1-0-0.dta") allbus_2021_stata ``` .right[↪️] --- class: middle ``` ## # A tibble: 5,342 × 542 ## za_nr doi version respid subst…¹ mode splt21 eastw…² german ep01 ep03 ep04 ep06 lm01 lm02 lm19 lm20 ## <dbl+lbl> <chr> <chr> <dbl> <dbl+l> <dbl+l> <dbl+l> <dbl+l> <dbl+> <dbl+lbl> <dbl+lbl> <dbl+lbl> <dbl+lbl> <dbl+l> <dbl+lbl> <dbl+> <dbl+l> ## 1 5280 [ALLBUS … doi:… 1.0.0 … 1 1 [SIM… 4 [MAI… 2 [SPL… 1 [ALT… 1 [JA] 3 [TEI… 3 [TEI… 3 [GLE… 4 [ETW… 2 [AN … 210 1 [JA] 2 [AN … ## 2 5280 [ALLBUS … doi:… 1.0.0 … 2 1 [SIM… 4 [MAI… 1 [SPL… 1 [ALT… 1 [JA] 3 [TEI… 1 [SEH… 3 [GLE… 3 [GLE… 5 [AN … 90 1 [JA] 5 [AN … ## 3 5280 [ALLBUS … doi:… 1.0.0 … 3 1 [SIM… 4 [MAI… 1 [SPL… 1 [ALT… 1 [JA] 3 [TEI… 1 [SEH… 2 [ETW… 3 [GLE… 6 [AN … 135 1 [JA] 5 [AN … ## 4 5280 [ALLBUS … doi:… 1.0.0 … 4 1 [SIM… 4 [MAI… 2 [SPL… 2 [NEU… 1 [JA] 2 [GUT] 1 [SEH… 1 [WES… 1 [WES… 7 [AN … 60 1 [JA] 7 [AN … ## 5 5280 [ALLBUS … doi:… 1.0.0 … 5 1 [SIM… 4 [MAI… 2 [SPL… 1 [ALT… 1 [JA] 3 [TEI… 3 [TEI… 3 [GLE… -8 [SPL… 7 [AN … 180 1 [JA] 7 [AN … ## 6 5280 [ALLBUS … doi:… 1.0.0 … 6 1 [SIM… 3 [CAW… 2 [SPL… 1 [ALT… 1 [JA] 2 [GUT] 1 [SEH… 2 [ETW… 3 [GLE… 5 [AN … 45 1 [JA] 3 [AN … ## 7 5280 [ALLBUS … doi:… 1.0.0 … 7 2 [SEQ… 3 [CAW… 3 [SPL… 1 [ALT… 1 [JA] -11 [TNZ… -11 [TNZ… -11 [TNZ… -11 [TNZ… 3 [AN … 30 1 [JA] 3 [AN … ## 8 5280 [ALLBUS … doi:… 1.0.0 … 8 1 [SIM… 4 [MAI… 2 [SPL… 1 [ALT… 1 [JA] 3 [TEI… 2 [GUT] 4 [ETW… 4 [ETW… 7 [AN … -9 [KEI… 1 [JA] 7 [AN … ## 9 5280 [ALLBUS … doi:… 1.0.0 … 9 2 [SEQ… 3 [CAW… 3 [SPL… 2 [NEU… 1 [JA] -11 [TNZ… -11 [TNZ… -11 [TNZ… -11 [TNZ… 7 [AN … 180 1 [JA] 7 [AN … ## 10 5280 [ALLBUS … doi:… 1.0.0 … 10 1 [SIM… 4 [MAI… 2 [SPL… 1 [ALT… 1 [JA] 3 [TEI… 2 [GUT] 3 [GLE… 3 [GLE… 7 [AN … 180 1 [JA] 7 [AN … ## # … with 5,332 more rows, 525 more variables: lm21 <dbl+lbl>, lm22 <dbl+lbl>, lm14 <dbl+lbl>, xr19 <dbl+lbl>, xr20 <dbl+lbl>, lm27 <dbl+lbl>, ## # lm28 <dbl+lbl>, lm29 <dbl+lbl>, lm30 <dbl+lbl>, lm31 <dbl+lbl>, lm32 <dbl+lbl>, lm33 <dbl+lbl>, lm34 <dbl+lbl>, lm35 <dbl+lbl>, lm36 <dbl+lbl>, ## # lm37 <dbl+lbl>, lm38 <dbl+lbl>, lm39 <dbl+lbl>, la01 <dbl+lbl>, id02 <dbl+lbl>, id01 <dbl+lbl>, mi05 <dbl+lbl>, mi06 <dbl+lbl>, mi07 <dbl+lbl>, ## # mi08 <dbl+lbl>, mi09 <dbl+lbl>, mi10 <dbl+lbl>, mi11 <dbl+lbl>, sex <dbl+lbl>, mborn <dbl+lbl>, yborn <dbl+lbl>, age <dbl+lbl>, agec <dbl+lbl>, ## # dn07 <dbl+lbl>, dm02 <dbl+lbl>, dm02c <dbl+lbl>, dm03 <dbl+lbl>, dg10 <dbl+lbl>, dg03 <dbl+lbl>, dm06 <dbl+lbl>, dn01 <dbl+lbl>, dn02 <dbl+lbl>, ## # dn04 <dbl+lbl>, dn05 <dbl+lbl>, ma01b <dbl+lbl>, ma02 <dbl+lbl>, ma03 <dbl+lbl>, ma04 <dbl+lbl>, mc01 <dbl+lbl>, mc02 <dbl+lbl>, mc03 <dbl+lbl>, ## # mc04 <dbl+lbl>, pn11 <dbl+lbl>, fr07 <dbl+lbl>, fr08 <dbl+lbl>, fr03b <dbl+lbl>, fr04b <dbl+lbl>, fr05b <dbl+lbl>, fr09 <dbl+lbl>, … ## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names ``` --- ## *SPSS* files with `haven` The `haven` package also offers the function `read_spss()` for importing *SPSS* files. The package also offers capabilities for handling *SPSS*-defined missing values by setting the option `user_na = TRUE` (default is `FALSE`). *Note*: The [`sjlabelled` package](https://cran.r-project.org/web/packages/sjlabelled/index.html) can also be used for [working with user-defined missings from *SPSS* files](https://cran.r-project.org/web/packages/sjlabelled/vignettes/intro_sjlabelled.html). **We will come back to *Stata* and *SPSS* files in a bit as they represent a specific file format in `R`: labelled data.** --- ## Other data import options These were just some very first examples of applying functions for data import from the different packages. There are many more... .pull-left[ `readr` - `read_csv()` - `read_tsv()` - `read_delim()` - `read_fwf()` - `read_table()` - `read_log()` ] .pull-right[ `haven` - `read_sas()` - `read_spss()` - `read_stata()` ] Not to mention all the helper functions and options. For example, we can define the cells to read from an *Excel* file by specifying the option `range = "C1:E4"` in `read_excel()` --- ## Data type specifications for `tibbles` - characters - indicated by `<chr>` - specified by `col_character()` - integers - indicated by `<int>` - specified by `col_integer()` - doubles - indicated by `<dbl>` - specified by `col_double()` - factors - indicated by `<fct>` - specified by `col_factor()` - logical - indicated by `<lgl>` - specified by `col_logical()` --- ## Changing variable types As mentioned before, `read_csv` 'guesses' the variable types by scanning the first 1000 observations. **NB**: This can go wrong! Luckily, we can change the variable type... - before/while loading the data - and after loading the data --- ## While loading the data in `read_csv` ```r titanic <- read_csv( "./data/titanic.csv", col_types = cols( PassengerId = col_double(), Survived = col_double(), Pclass = col_double(), Name = col_character(), Sex = col_character(), Age = col_double(), SibSp = col_double(), Parch = col_double(), Ticket = col_character(), Fare = col_double(), Cabin = col_character(), Embarked = col_character() ) ) titanic ``` .right[↪️] --- class: middle ``` ## # A tibble: 891 × 12 ## PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked ## <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <chr> <chr> ## 1 1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.25 <NA> S ## 2 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0 PC 17599 71.3 C85 C ## 3 3 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7.92 <NA> S ## 4 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 113803 53.1 C123 S ## 5 5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.05 <NA> S ## 6 6 0 3 Moran, Mr. James male NA 0 0 330877 8.46 <NA> Q ## 7 7 0 1 McCarthy, Mr. Timothy J male 54 0 0 17463 51.9 E46 S ## 8 8 0 3 Palsson, Master. Gosta Leonard male 2 3 1 349909 21.1 <NA> S ## 9 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27 0 2 347742 11.1 <NA> S ## 10 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14 1 0 237736 30.1 <NA> C ## # … with 881 more rows ## # ℹ Use `print(n = ...)` to see more rows ``` --- ## While loading the data in `read_csv` ```r titanic <- read_csv( "./data/titanic.csv", col_types = cols( PassengerId = col_double(), Survived = col_double(), Pclass = col_double(), Name = col_character(), Sex = col_factor(), # This one changed! Age = col_double(), SibSp = col_double(), Parch = col_double(), Ticket = col_character(), Fare = col_double(), Cabin = col_character(), Embarked = col_character() ) ) titanic ``` .right[↪️] --- class: middle ``` ## # A tibble: 891 × 12 ## PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked ## <dbl> <dbl> <dbl> <chr> <fct> <dbl> <dbl> <dbl> <chr> <dbl> <chr> <chr> ## 1 1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.25 <NA> S ## 2 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0 PC 17599 71.3 C85 C ## 3 3 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7.92 <NA> S ## 4 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 113803 53.1 C123 S ## 5 5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.05 <NA> S ## 6 6 0 3 Moran, Mr. James male NA 0 0 330877 8.46 <NA> Q ## 7 7 0 1 McCarthy, Mr. Timothy J male 54 0 0 17463 51.9 E46 S ## 8 8 0 3 Palsson, Master. Gosta Leonard male 2 3 1 349909 21.1 <NA> S ## 9 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27 0 2 347742 11.1 <NA> S ## 10 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14 1 0 237736 30.1 <NA> C ## # … with 881 more rows ## # ℹ Use `print(n = ...)` to see more rows ``` --- ## After loading the data ```r titanic <- type_convert( titanic, col_types = cols( PassengerId = col_double(), Survived = col_double(), Pclass = col_double(), Name = col_character(), Sex = col_factor(), Age = col_double(), SibSp = col_double(), Parch = col_double(), Ticket = col_character(), Fare = col_double(), Cabin = col_character(), Embarked = col_character() ) ) ``` --- ## Beyond flat files: labelled data A lot of data comes in some sort of flat file format, such as `CSV`. In the social sciences, however, we often deal with proprietary file formats, such as *SPSS*'s `.sav` or *Stata*'s `.dta` files. What these data typically include are labels. These labels are used to describe variables or variable values. They comprise some specific metadata inherent in these proprietary file formats. *If you were able to travel back ten years in time and ask an `R` geek, she'd say that you cannot use labels in R. You'd either have to import, e.g., value labels as character strings or use their codes as factors. However, these days...* --- ## Not being able to use labelled data is a thing of the past Nowadays, if you use the `haven` package, labels are built-in for the corresponding file types. For example: ```r allbus_2021 <- haven::read_sav("./data/allbus_2021/ZA5280_v1-0-0.sav") allbus_2021["agec"] ``` ``` ## # A tibble: 5,342 × 1 ## agec ## <dbl+lbl> ## 1 3 [45-59 JAHRE] ## 2 3 [45-59 JAHRE] ## 3 5 [75-89 JAHRE] ## 4 5 [75-89 JAHRE] ## 5 4 [60-74 JAHRE] ## 6 1 [18-29 JAHRE] ## 7 2 [30-44 JAHRE] ## 8 3 [45-59 JAHRE] ## 9 4 [60-74 JAHRE] ## 10 3 [45-59 JAHRE] ## # … with 5,332 more rows ## # ℹ Use `print(n = ...)` to see more rows ``` --- ## Advantages of using labelled data One could rejoice in not having to use a codebook anymore, just like in *SPSS* (although just looking at code output for glimpsing feels much more... data-geeky). An advantage is definitely that you can potentially re-use the labels in figures and plots, and some `R` packages do that automatically, such as the [`sjPlot`](https://strengejacke.github.io/sjPlot/) package. In addition, when you exchange your data with colleagues who do not use `R` or when you plan to publish your data (which you always should if that is possible), being able to export data you have manipulated in `R` in different formats is great. **However, be aware of the missing values hell that you may enter due to different missing value definitions in *Stata* and *SPSS*.** --- ## Getting labels For variables: ```r sjlabelled::get_label(allbus_2021$agec) ``` ``` ## [1] "ALTER: BEFRAGTE(R), KATEGORISIERT" ``` For values: .tinyish[ ```r sjlabelled::get_labels(allbus_2021$agec) ``` ``` ## [1] "NICHT GENERIERBAR" "18-29 JAHRE" "30-44 JAHRE" "45-59 JAHRE" "60-74 JAHRE" "75-89 JAHRE" "UEBER 89 JAHRE" ``` ] --- ## Setting labels: Variables ```r allbus_2021$agec <- sjlabelled::set_label(allbus_2021$agec, label = "Age, categorized") sjlabelled::get_label(allbus_2021$agec) ``` ``` ## [1] "Age, categorized" ``` --- ## Setting labels: Values .tinyish[ ```r allbus_2021$agec <- sjlabelled::set_labels( allbus_2021$agec, labels = c( "18-29 years", "30-44 years", "45-59 years", "60-74 years", "75-89 years", "Over 89 years" ) ) sjlabelled::get_labels(allbus_2021$agec) ``` ``` ## [1] "18-29 years" "30-44 years" "45-59 years" "60-74 years" "75-89 years" "Over 89 years" ``` ] --- class: center, middle # [Exercise](https://jobreu.github.io/r-intro-gesis-2021/exercises/Exercise_1_2_2_Flat_Files.html) time 🏋️‍♀️💪🏃🚴 ## [Solutions](https://jobreu.github.io/r-intro-gesis-2021/solutions/Exercise_1_2_2_Flat_Files.html) --- ## Exporting data Sometimes our data have to leave `R`, for example, if we.... - share data with colleagues who do not use `R` - want to continue where we left off - particularly if data wrangling took a long time For such purposes, we also need a way to export our data. All of the packages we have discussed in this session also have designated functions for that. <img src="data:image/png;base64,#../img/export_data.png" width="50%" style="display: block; margin: auto;" /> --- ## Examples: CSV and Stata files ```r write_csv(titanic, "titanic_own.csv") ``` ```r write_dta(titanic, "titanic_own.dta") ``` --- ## `R`'s native file formats If you plan to continue to work with `R` (something we would always recommend 😜), there are at least two native 'file formats' to choose from. The advantage of using them is that they are compressed files, so that they don't occupy unnecessarily large disk space. These two formats are `.Rdata`/`.rda` and `.rds`. The key difference between them is that `.rds` can only hold one object, whereas `.Rdata`/`.rda` can also be used for storing several objects in one file. --- ## `.Rdata`/`.rda` Saving ```r save(mydata, file = "mydata.RData") ``` Loading ```r load("mydata.RData") ``` --- ## `.rds` Saving ```r saveRDS(mydata, "mydata.rds") ``` Loading ```r mydata <- readRDS("mydata.rds") ``` *Note*: A nice property of `saveRDS()` is that just saves a representation of the object, which means you can name it whatever you want when loading. --- ## Saving just everything If you have not changed the General Global Options in *RStudio* as suggested in the *Getting Started* session, you may have noticed that, when closing *Rstudio*, by default, the programs asks you whether you want to save the workspace image. <img src="data:image/png;base64,#../img/save_image.png" width="50%" style="display: block; margin: auto;" /> You can also do that whenever you want using the `save.image()` function: ```r save.image(file = "my_fancy_workspace.RData") ``` .small[ *Note*: As we've said before, though, this is not something we'd recommend as a worfklow. Instead, you should (explicitly and separately) save your `R` scripts and data sets (in appropriate formats). ] --- ## Additional packages Besides `readr`, `haven` and `readxl`, there also are some other packages that facilitate importing specific data types as tibbles: - [`sjlabelled`](https://cran.r-project.org/web/packages/sjlabelled/index.html) for labelled data, e.g., from *SPSS* or *Stata* - [`sf`](https://github.com/r-spatial/sf) for geospatial data --- ## Other packages for data import For data import (and export) in general, there are even more options, such as... - `base` R - the [`foreign` package](https://cran.r-project.org/web/packages/foreign/index.html) for *SPSS* and *Stata* files - [`data.table`](https://cran.r-project.org/web/packages/data.table/index.html) or [`fst`](https://www.fstpackage.org/) for large data sets - [`jsonlite`](https://cran.r-project.org/web/packages/jsonlite/index.html) for `.json` files - [`datapasta`](https://github.com/MilesMcBain/datapasta) for copying and pasting data into tribbles (e.g., from websites, *Excel* or *Word* files) --- ## Reminder regarding file paths In general, you should avoid using absolute file paths to maintain your code reproducible and future-proof. We already talked about this in the introduction, but this is particularly important for importing and exporting data. As a reminder: Absolute file paths look like this (on different OS): ```r # Windows load("C:/Users/cool_user/data/fancy_data.Rdata") # Mac load("/Users/cool_user/data/fancy_data.Rdata") # GNU/Linux load("/home/cool_user/data/fancy_data.Rdata") ``` --- ## Use relative paths Instead of using absolute paths, it is recommended to use relative file paths. The general principle here is to start from a directory where your current script currently exists and navigate to your target location. Say we are in the "C:/Users/cool_user/" location on a Windows machine. To load your data, we would use: ```r load("./data/fancy_data.Rdata") ``` If we were in a different folder, e.g., "C:/Users/cool_user/cat_pics/mittens/", we would use: ```r load("../../data/fancy_data.Rdata") ``` --- class: center, middle Please first download the [GGSS 2021](https://search.gesis.org/research_data/ZA5280) as .sav, .dta, and .csv file. # [Exercise](https://jobreu.github.io/r-intro-gesis-2021/exercises/Exercise_1_2_3_Statistical_Software_Files.html) time 🏋️‍♀️💪🏃🚴 ## [Solutions](https://jobreu.github.io/r-intro-gesis-2021/solutions/Exercise_1_2_3_Statistical_Software_Files.html)